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CNN Based Detectors on Planetary Environments: A Performance Evaluation

An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environmen...

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Autores principales: Furlán, Federico, Rubio, Elsa, Sossa, Humberto, Ponce, Víctor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661793/
https://www.ncbi.nlm.nih.gov/pubmed/33192440
http://dx.doi.org/10.3389/fnbot.2020.590371
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author Furlán, Federico
Rubio, Elsa
Sossa, Humberto
Ponce, Víctor
author_facet Furlán, Federico
Rubio, Elsa
Sossa, Humberto
Ponce, Víctor
author_sort Furlán, Federico
collection PubMed
description An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors.
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spelling pubmed-76617932020-11-13 CNN Based Detectors on Planetary Environments: A Performance Evaluation Furlán, Federico Rubio, Elsa Sossa, Humberto Ponce, Víctor Front Neurorobot Neuroscience An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors. Frontiers Media S.A. 2020-10-30 /pmc/articles/PMC7661793/ /pubmed/33192440 http://dx.doi.org/10.3389/fnbot.2020.590371 Text en Copyright © 2020 Furlán, Rubio, Sossa and Ponce. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Furlán, Federico
Rubio, Elsa
Sossa, Humberto
Ponce, Víctor
CNN Based Detectors on Planetary Environments: A Performance Evaluation
title CNN Based Detectors on Planetary Environments: A Performance Evaluation
title_full CNN Based Detectors on Planetary Environments: A Performance Evaluation
title_fullStr CNN Based Detectors on Planetary Environments: A Performance Evaluation
title_full_unstemmed CNN Based Detectors on Planetary Environments: A Performance Evaluation
title_short CNN Based Detectors on Planetary Environments: A Performance Evaluation
title_sort cnn based detectors on planetary environments: a performance evaluation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661793/
https://www.ncbi.nlm.nih.gov/pubmed/33192440
http://dx.doi.org/10.3389/fnbot.2020.590371
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